This chapter introduces Sparse Distributed Representations (SDRs) as a biologically inspired and computationally efficient mechanism for information encoding. Drawing on principles such as sparsity, hierarchy, and plasticity observed in the neocortex, SDRs emulate how the brain processes and stores information through sparse patterns of neural activity. Unlike dense representations typical in conventional AI systems, SDRs offer robustness, noise tolerance, and energy efficiency, making them well-suited for learning, generalization, and pattern recognition. The chapter defines SDRs as high-dimensional binary vectors with a small proportion of active bits, enabling fault-tolerant and semantically meaningful representations. It explores key operations such as overlap-based similarity, subsampling for efficiency, and the union operation for compact set representation. Additionally, it discusses the capacity and scalability of SDRs, emphasizing their low risk of false positives. By leveraging bitwise operations and sparse data structures, SDRs provide a practical and biologically grounded framework for developing AI systems that are both efficient and adaptable.